Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Pregnant is highly overall correlated with AgeHigh correlation
Triceps skin fold thickness is highly overall correlated with 2-Hour serum insulinHigh correlation
2-Hour serum insulin is highly overall correlated with Triceps skin fold thicknessHigh correlation
Age is highly overall correlated with PregnantHigh correlation
Pregnant has 111 (14.5%) zerosZeros
Diastolic blood pressure has 35 (4.6%) zerosZeros
Triceps skin fold thickness has 227 (29.6%) zerosZeros
2-Hour serum insulin has 374 (48.7%) zerosZeros
Body mass index has 11 (1.4%) zerosZeros

Reproduction

Analysis started2023-09-23 21:38:36.680234
Analysis finished2023-09-23 21:38:46.981409
Duration10.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Pregnant
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:47.087909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2023-09-23T18:38:47.227172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

plasma glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.89453
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:47.411587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.972618
Coefficient of variation (CV)0.26446703
Kurtosis0.64077982
Mean120.89453
Median Absolute Deviation (MAD)20
Skewness0.1737535
Sum92847
Variance1022.2483
MonotonicityNot monotonic
2023-09-23T18:38:47.601510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
129 14
 
1.8%
125 14
 
1.8%
106 14
 
1.8%
95 13
 
1.7%
102 13
 
1.7%
105 13
 
1.7%
108 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 2
 
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

Diastolic blood pressure
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.105469
Minimum0
Maximum122
Zeros35
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:47.774739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.355807
Coefficient of variation (CV)0.28009082
Kurtosis5.1801566
Mean69.105469
Median Absolute Deviation (MAD)8
Skewness-1.843608
Sum53073
Variance374.64727
MonotonicityNot monotonic
2023-09-23T18:38:48.085634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
68 45
 
5.9%
78 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
0 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
0 35
4.6%
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
 
1.7%
52 11
 
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

Triceps skin fold thickness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.536458
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:48.258909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.952218
Coefficient of variation (CV)0.77677549
Kurtosis-0.52007187
Mean20.536458
Median Absolute Deviation (MAD)12
Skewness0.1093725
Sum15772
Variance254.47325
MonotonicityNot monotonic
2023-09-23T18:38:48.430836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
28 20
 
2.6%
33 20
 
2.6%
18 20
 
2.6%
31 19
 
2.5%
39 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
0 227
29.6%
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
 
1.4%
14 6
 
0.8%
15 14
 
1.8%
16 6
 
0.8%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

2-Hour serum insulin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.799479
Minimum0
Maximum846
Zeros374
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:48.603614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3127.25
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)127.25

Descriptive statistics

Standard deviation115.244
Coefficient of variation (CV)1.4441699
Kurtosis7.2142596
Mean79.799479
Median Absolute Deviation (MAD)30.5
Skewness2.2722509
Sum61286
Variance13281.18
MonotonicityNot monotonic
2023-09-23T18:38:48.776185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 374
48.7%
105 11
 
1.4%
140 9
 
1.2%
130 9
 
1.2%
120 8
 
1.0%
180 7
 
0.9%
100 7
 
0.9%
94 7
 
0.9%
135 6
 
0.8%
110 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
0 374
48.7%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
 
0.3%
22 1
 
0.1%
23 2
 
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

Body mass index
Real number (ℝ)

ZEROS 

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.992578
Minimum0
Maximum67.1
Zeros11
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:48.953683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32
Q336.6
95-th percentile44.395
Maximum67.1
Range67.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.8841603
Coefficient of variation (CV)0.24643717
Kurtosis3.2904429
Mean31.992578
Median Absolute Deviation (MAD)4.6
Skewness-0.42898159
Sum24570.3
Variance62.159984
MonotonicityNot monotonic
2023-09-23T18:38:49.123951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.2 12
 
1.6%
31.6 12
 
1.6%
0 11
 
1.4%
32.4 10
 
1.3%
33.3 10
 
1.3%
32.9 9
 
1.2%
30.1 9
 
1.2%
32.8 9
 
1.2%
30.8 9
 
1.2%
Other values (238) 664
86.5%
ValueCountFrequency (%)
0 11
1.4%
18.2 3
 
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
 
0.3%
19.6 3
 
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%
Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:49.312619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2023-09-23T18:38:49.505656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.261 5
 
0.7%
0.268 5
 
0.7%
0.238 5
 
0.7%
0.207 5
 
0.7%
0.259 5
 
0.7%
0.284 4
 
0.5%
0.263 4
 
0.5%
0.299 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-09-23T18:38:49.703897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2023-09-23T18:38:49.902231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

target
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2023-09-23T18:38:50.067033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T18:38:50.203382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2023-09-23T18:38:45.599078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:37.621676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.948322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.142028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.332347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.367353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.383731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.563991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.735830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:37.807983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.089513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.330582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.470260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.500751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.517181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.694539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.882309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:37.950728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.246549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.503294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.613693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.648860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.783371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.832682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.999516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.092851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.420188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.667667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.733032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.767263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.910363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.966714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:46.133727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.234205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.573424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.787868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.866015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.891760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.031824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.095212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:46.252913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.408498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.730900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:40.915740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.982584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.000686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.152283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.219064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:46.383678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.660347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.874056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.049892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.116087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.132383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.302244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.347726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:46.512354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:38.806922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:39.999905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:41.188872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:42.249237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:43.249545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:44.430585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-23T18:38:45.480611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-23T18:38:50.304949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Pregnantplasma glucoseDiastolic blood pressureTriceps skin fold thickness2-Hour serum insulinBody mass indexDiabetes pedigree functionAgetarget
Pregnant1.0000.1310.185-0.085-0.1270.000-0.0430.6070.235
plasma glucose0.1311.0000.2350.0600.2130.2310.0910.2850.487
Diastolic blood pressure0.1850.2351.0000.126-0.0070.2930.0300.3510.152
Triceps skin fold thickness-0.0850.0600.1261.0000.5410.4440.180-0.0670.208
2-Hour serum insulin-0.1270.213-0.0070.5411.0000.1930.221-0.1140.159
Body mass index0.0000.2310.2930.4440.1931.0000.1410.1310.317
Diabetes pedigree function-0.0430.0910.0300.1800.2210.1411.0000.0430.173
Age0.6070.2850.351-0.067-0.1140.1310.0431.0000.314
target0.2350.4870.1520.2080.1590.3170.1730.3141.000

Missing values

2023-09-23T18:38:46.685925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-23T18:38:46.881086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Pregnantplasma glucoseDiastolic blood pressureTriceps skin fold thickness2-Hour serum insulinBody mass indexDiabetes pedigree functionAgetarget
04.0117.062.012.00.029.70.38030.01
14.0158.078.00.00.032.90.80331.01
22.0118.080.00.00.042.90.69321.01
313.0129.00.030.00.039.90.56944.01
45.0162.0104.00.00.037.70.15152.01
57.0114.064.00.00.027.40.73234.01
66.0102.082.00.00.030.80.18036.01
71.0196.076.036.0249.036.50.87529.01
89.0102.076.037.00.032.90.66546.01
97.0161.086.00.00.030.40.16547.01
Pregnantplasma glucoseDiastolic blood pressureTriceps skin fold thickness2-Hour serum insulinBody mass indexDiabetes pedigree functionAgetarget
7585.0132.080.00.00.026.80.18669.00
7599.091.068.00.00.024.20.20058.00
7603.0128.078.00.00.021.10.26855.00
7610.0108.068.020.00.027.30.78732.00
7622.0112.068.022.094.034.10.31526.00
7631.081.074.041.057.046.31.09632.00
7644.094.065.022.00.024.70.14821.00
7653.0158.064.013.0387.031.20.29524.00
7660.057.060.00.00.021.70.73567.00
7674.095.060.032.00.035.40.28428.00